Shaping Professional Identity Through Professional Development: A Retrospective Study of TESOL Professionals
Bibliographic record
Abstract
In an educational milieu student learning outcomes are directly related to teacher professional identity (TPI) i.e. improvement in the TPI will bear a direct positive effect on the learners’ academic achievements. Current study focuses on the development of TPI of English as a Foreign Language Teacher at English Language Institute (ELI) of a Saudi Arabian university through an in-service Cambridge English Teachers (CET) Professional Development (PD) program (CET-PD). Five determinants of TPI - Knowledge of Teaching Context (KCT), Collegial Collaboration (CC), Teaching Practices (TPs), Teacher Self-Efficacy (TSE), and Teacher Agency (TA) were studied before and after the PD program. Retrospective pretest-posttest research design was employed for addressing the research question: whereas responses on the five determinants were elicited from 120 participants through a self-administered questionnaire before and after the CET-PD program. Due to non-normality of data, a non-parametric statistic test-Wilcoxon signed Rank test was employed to analyze the collected data using SPSS. Results of the study revealed that three determinants of TPI - KCT, TSE, and TPs exhibited larger differences; whereas, for CC the differences were moderate and for TA the differences were minimal. By and large, due to in-service CET- PD program TPI exhibited improvement. The results of the study will be beneficial for teacher trainers to focus more on the teachers’ awareness of the learners’ and institutional contextual knowledge in a culture embedded in conservative norms. This study is a part of the quantitative phase of an ongoing Ph.D. project which employs mixed method convergent design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".